Machine Learning

Risks maps are heat map or data visualization tools for communication of specific risks which an organization face. Companies are involved in identification and prioritization of risks which are associated with businesses. If companies are involved in the improvement of understanding of risk appetite and profiling of it. It’s also important to clarify the nature and risk impact on businesses. …

Historically, data discovery has existed at the nexus point between data preparation and analytics. The discovery process was frequently viewed as the means of gathering the requisite data for analytics while illustrating relationships between data elements which might inform them. Today, data discovery’s utility has considerably broadened. Aided by machine learning and data cataloging techniques, data discovery is playing an …

Artificial Intelligence may well be the most influential suite of technologies to impact the enterprise for the next several years. Its learning capabilities and decision support propensity are horizontally acclaimed; many consider machine learning a panacea for data-driven processes in general. Still, an often neglected aspect of the transformative impact of AI is exactly how to implement it to gain …

In contemporary business settings, Artificial Intelligence is largely synonymous with machine learning’s classic or advanced neural network capabilities. Several aspects of predictive analytics, timely recommendations, and sophisticated pattern recognition are attributed to these manifestations of machine learning, which many consider to be the summation of AI’s value to the enterprise today. On the one hand, such a perception is certainly …

Numbers don’t lie. According to CB Insights, 100 of the most promising private startups focused on Artificial Intelligence raised $11.7 billion in equity funding in 367 deals during 2017. Several of those companies focus on deep learning technologies, including the most well-funded, ByteDance, which accounts for over a fourth of 2017’s private startup funding with 3.1 billion dollars raised. In …

The big data ecosystem will continue to evolve and expand in the coming year, with a number of new developments pertaining to decentralized and centralized paradigms, automation, Artificial Intelligence, Blockchain, and the Internet of Things. Jelani Harper elucidates these trends and others in The next wave of big data technology: distributed automation.

The self-service movement has expanded from Business Intelligence to encompass considerable regions of data science and supporting domains such as data preparation, data curation, and data engineering. The most definitive aspects of self-service BI and data science include: Data discovery – The data discovery process is that wherein users determine what data is available for a particular use case; it’s …

Machine learning is unequivocally the foundation of Artificial Intelligence’s resurgence. The capability to utilize intelligent algorithms to make predictions—and to dynamically refine and improve those predictions based on current and future results—is perhaps the most utilitarian manifestation of AI today. The applicability of this penchant for learning is so illimitable largely due to the nature of data-driven needs. Machine learning …

If the defining characteristic of data management in 2018 is the heterogeneity of contemporary computing environments, then Blockchain is a considerable factor contributing to its decentralization. Expectations for this distributed ledger technology are decidedly high. Its low latency, irrefutable transaction capabilities are overtaking so many verticals that one of Forrester’s Top 10 Technology Trends To Watch: 2018 to 2020 predicts …

The collective form of Artificial Intelligence is evolving. Granted, it’s still a fundamental aspect of big data, analytics, and numerous cloud deployments. However, its propensity for personalizing all experiences of the enterprise—for both employees and customers, internally and externally—will surge forward in the coming year in a number of distinct ways. Previously, the most prevalent manifestations of AI included cognitive …

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